2016

The International Journal of Robotics Research, 35(14):1731-1749, December 2016 (article)

Abstract

The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. The GF represents the belief of the current state by a Gaussian distribution, whose mean is an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF as the solution to a constrained optimization problem. From this new perspective, the GF is seen as a special case of a much broader class of filters, obtained by relaxing the constraint on the form of the approximate posterior. On this basis, we outline some conditions which potential generalizations have to satisfy in order to maintain the computational efficiency of the GF. We propose one concrete generalization which corresponds to the standard GF using a pseudo measurement instead of the actual measurement. Extending an existing GF implementation in this manner is trivial. Nevertheless, we show that this small change can have a major impact on the estimation accuracy.

The invention comprises a learned model of human body shape and pose dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity- dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. The invention quantitatively evaluates variants of SMPL using linear or dual- quaternion blend skinning and show that both are more accurate than a Blend SCAPE model trained on the same data. In a further embodiment, the invention realistically models dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

Miniaturization calls for micro-actuators that can be powered wirelessly and addressed individually. Here, we develop functional surfaces consisting of arrays of acoustically resonant microcavities, and we demonstrate their application as two-dimensional wireless actuators. When remotely powered by an acoustic field, the surfaces provide highly directional propulsive forces in fluids through acoustic streaming. A maximal force of similar to 0.45mN is measured on a 4 x 4 mm(2) functional surface. The response of the surfaces with bubbles of different sizes is characterized experimentally. This shows a marked peak around the micro-bubbles' resonance frequency, as estimated by both an analytical model and numerical simulations. The strong frequency dependence can be exploited to address different surfaces with different acoustic frequencies, thus achieving wireless actuation with multiple degrees of freedom. The use of the functional surfaces as wireless ready-to-attach actuators is demonstrated by implementing a wireless and bidirectional miniaturized rotary motor, which is 2.6 x 2.6 x 5 mm(3) in size and generates a stall torque of similar to 0.5 mN.mm. The adoption of micro-structured surfaces as wireless actuators opens new possibilities in the development of miniaturized devices and tools for fluidic environments that are accessible by low intensity ultrasound fields.

The use of bacterial cells as agents of medical therapy has a long history. Research that was ignited over a century ago with the accidental infection of cancer patients has matured into a platform technology that offers the promise of opening up new potential frontiers in medical treatment. Bacterial cells exhibit unique characteristics that make them well-suited as smart drug delivery agents. Our ability to genetically manipulate the molecular machinery of these cells enables the customization of their therapeutic action as well as its precise tuning and spatio-temporal control, allowing for the design of unique, complex therapeutic functions, unmatched by current drug delivery systems. Early results have been promising, but there are still many important challenges that must be addressed. We present a review of promises and challenges of employing bioengineered bacteria in drug delivery systems and introduce the biohybrid design concept as a new additional paradigm in bacteria-based drug delivery.

This minireview discusses whether catalytically active macromolecules and abiotic nanocolloids, that are smaller than motile bacteria, can self-propel. Kinematic reversibility at low Reynolds number demands that self-propelling colloids must break symmetry. Methods that permit the synthesis and fabrication of Janus nanocolloids are therefore briefly surveyed, as well as means that permit the analysis of the nanocolloids' motion. Finally, recent work is reviewed which shows that nanoagents are small enough to penetrate the complex inhomogeneous polymeric network of biological fluids and gels, which exhibit diverse rheological behaviors.

Brief verbal descriptions of bodies (e.g. curvy, long-legged) can elicit vivid mental images. The ease with which we create these mental images belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and show that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2094 bodies. This allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape, capturing perceptually salient global and local body features.

Microorganisms move in challenging environments by periodic changes in body shape. In contrast, current artificial microrobots cannot actively deform, exhibiting at best passive bending under external fields. Here, by taking advantage of the wireless, scalable and spatiotemporally selective capabilities that light allows, we show that soft microrobots consisting of photoactive liquid-crystal elastomers can be driven by structured monochromatic light to perform sophisticated biomimetic motions. We realize continuum yet selectively addressable artificial microswimmers that generate travelling-wave motions to self-propel without external forces or torques, as well as microrobots capable of versatile locomotion behaviours on demand. Both theoretical predictions and experimental results confirm that multiple gaits, mimicking either symplectic or antiplectic metachrony of ciliate protozoa, can be achieved with single microswimmers. The principle of using structured light can be extended to other applications that require microscale actuation with sophisticated spatiotemporal coordination for advanced microrobotic technologies.

This paper introduces a new five-dimensional localization method for an untethered meso-scale magnetic robot, which is manipulated by a computer-controlled electromagnetic system. The developed magnetic localization setup is a two-dimensional array of mono-axial Hall-effect sensors, which measure the perpendicular magnetic fields at their given positions. We introduce two steps for localizing a magnetic robot more accurately. First, the dipole modeled magnetic field of the electromagnet is subtracted from the measured data in order to determine the robot's magnetic field. Secondly, the subtracted magnetic field is twice differentiated in the perpendicular direction of the array, so that the effect of the electromagnetic field in the localization process is minimized. Five variables regarding the position and orientation of the robot are determined by minimizing the error between the measured magnetic field and the modeled magnetic field in an optimization method. The resulting position error is 2.1±0.8 mm and angular error is 6.7±4.3° within the applicable range (5 cm) of magnetic field sensors at 200 Hz. The proposed localization method would be used for the position feedback control of untethered magnetic devices or robots for medical applications in the future.

There is an increasing demand for soft actuators because of their importance in soft robotics, artificial muscles, biomimetic devices, and beyond. However, the development of soft actuators capable of low-voltage operation, powerful actuation, and programmable shape-changing is still challenging. In this work, we propose programmable bilayer actuators that operate based on the large hygroscopic contraction of the copy paper and simultaneously large thermal expansion of the polypropylene film upon increasing the temperature. The electrothermally activated bending actuators can function with low voltages (≤ 8 V), low input electric power per area (P ≤ 0.14 W cm–2), and low temperature changes (≤ 35 °C). They exhibit reversible shape-changing behavior with curvature radii up to 1.07 cm–1 and bending angle of 360°, accompanied by powerful actuation. Besides the electrical activation, they can be powered by humidity or light irradiation. We finally demonstrate the use of our paper actuators as a soft gripper robot and a lightweight paper wing for aerial robotics.

Lab on a Chip, 16(22):4445-4457, Royal Society of Chemistry, October 2016 (article)

Abstract

At the sub-millimeter scale, capillary forces enable robust and reversible adhesion between biological organisms and varied substrates. Current human-engineered mobile untethered micromanipulation systems rely on forces which scale poorly or utilize gripper-part designs that promote manipulation. Capillary forces, alternatively, are dependent upon the surface chemistry (which is scale independent) and contact perimeter, which conforms to the part surface. We report a mobile capillary microgripper that is able to pick and place parts of various materials and geometries, and is thus ideal for microassembly tasks that cannot be accomplished by large tethered manipulators. We achieve the programmable assembly of sub-millimeter parts in an enclosed three-dimensional aqueous environment by creating a capillary bridge between the targeted part and a synthetic, untethered, mobile body. The parts include both hydrophilic and hydrophobic components: hydrogel, kapton, human hair, and biological tissue. The 200 μm untethered system can be controlled with five-degrees-of-freedom and advances progress towards autonomous desktop manufacturing for tissue engineering, complex micromachines, microfluidic devices, and meta-materials.

On page 2325, Ajay Vikram Singh and Metin Sitti propose a facile surface patterning technique and a specific, strong biotin–streptavidin bonding of bacteria on patterned surfaces to fabricate Janus particles that are propelled by the attached bacteria. Such bacteria-driven Janus microswimmers could be used for future medicine in targeted drug delivery and environmental remediation.

Immobilization of colloidal assemblies onto solid supports via a fast UV-triggered click-reaction is achieved. Transient assemblies of microparticles and colloidal materials can be captured and transferred to solid supports. The technique does not require complex reaction conditions, and is compatible with a variety of particle assembly methods.

We demonstrate that chiral magnesium nanoparticles show remarkable plasmonic extinction- and chiroptical-effects in the ultraviolet region. The Mg nanohelices possess an enhanced local surface plasmon resonance (LSPR) sensitivity due to the strong dispersion of most substances in the UV region.

Holographic techniques are fundamental to applications such as volumetric displays(1), high-density data storage and optical tweezers that require spatial control of intricate optical(2) or acoustic fields(3,4) within a three-dimensional volume. The basis of holography is spatial storage of the phase and/or amplitude profile of the desired wavefront(5,6) in a manner that allows that wavefront to be reconstructed by interference when the hologram is illuminated with a suitable coherent source. Modern computer-generated holography(7) skips the process of recording a hologram from a physical scene, and instead calculates the required phase profile before rendering it for reconstruction. In ultrasound applications, the phase profile is typically generated by discrete and independently driven ultrasound sources(3,4,8-12); however, these can only be used in small numbers, which limits the complexity or degrees of freedom that can be attained in the wavefront. Here we introduce monolithic acoustic holograms, which can reconstruct diffraction-limited acoustic pressure fields and thus arbitrary ultrasound beams. We use rapid fabrication to craft the holograms and achieve reconstruction degrees of freedom two orders of magnitude higher than commercial phased array sources. The technique is inexpensive, appropriate for both transmission and reflection elements, and scales well to higher information content, larger aperture size and higher power. The complex three-dimensional pressure and phase distributions produced by these acoustic holograms allow us to demonstrate new approaches to controlled ultrasonic manipulation of solids in water, and of liquids and solids in air. We expect that acoustic holograms will enable new capabilities in beam-steering and the contactless transfer of power, improve medical imaging, and drive new applications of ultrasound.

Direct detection of molecular chirality is practically impossible by methods of standard nuclear magnetic resonance (NMR) that is based on interactions involving magnetic-dipole and magnetic-field operators. However, theoretical studies provide a possible direct probe of chirality by exploiting an enantiomer selective additional coupling involving magnetic-dipole, magnetic-field, and electric field operators. This offers a way for direct experimental detection of chirality by nuclear magneto-electric resonance (NMER). This method uses both resonant magnetic and electric radiofrequency (RF) fields. The weakness of the chiral interaction though requires a large electric RF field and a small transverse RF magnetic field over the sample volume, which is a non-trivial constraint. In this study, we present a detailed study of the NMER concept and a possible experimental realization based on a loop-gap resonator. For this original device, the basic principle and numerical studies as well as fabrication and measurements of the frequency dependence of the scattering parameter are reported. By simulating the NMER spin dynamics for our device and taking the F-19 NMER signal of enantiomer-pure 1,1,1-trifluoropropan-2-ol, we predict a chirality induced NMER signal that accounts for 1%-5% of the standard achiral NMR signal. Published by AIP Publishing.

The adhesive system of geckos has inspired hundreds of synthetic adhesives. While this system has been used relentlessly as a source of inspiration, less work has been done in reverse, where synthetics are used to test questions and hypotheses about the natural system. Here we take such an approach. We tested shear adhesion of a mushroom-tipped synthetic gecko adhesive under conditions that produced perplexing results in the natural adhesive system. Synthetic samples were tested at two temperatures (12 °C and 32 °C) and four different humidity levels (30%, 55%, 70%, and 80% RH). Surprisingly, adhesive performance of the synthetic samples matched that of living geckos, suggesting that uncontrolled parameters in the natural system, such as surface chemistry and material changes, may not be as influential in whole-animal performance as previously thought. There was one difference, however, when comparing natural and synthetic adhesive performance. At 12 °C and 80% RH, adhesion of the synthetic structures was lower than expected based on the natural system’s performance. Our approach highlights a unique opportunity for both biologists and material scientists, where new questions and hypotheses can be fueled by joint comparisons of the natural and synthetic systems, ultimately improving knowledge of both.

The purpose of this thesis is the study of non-parametric models for structured data and their fields of application in computer vision. We aim at the development of context-sensitive architectures which are both expressive and efficient. Our focus is on directed graphical models, in particular Bayesian networks, where we combine the flexibility of non-parametric local distributions with the efficiency of a global topology with bounded treewidth. A bound on the treewidth is obtained by either constraining the maximum indegree of the underlying graph structure or by introducing determinism. The non-parametric distributions in the nodes of the graph are given by decision trees or kernel density estimators.
The information flow implied by specific network topologies, especially the resultant (conditional) independencies, allows for a natural integration and control of contextual information. We distinguish between three different types of context: static, dynamic, and semantic. In four different approaches we propose models which exhibit varying combinations of these contextual properties and allow modeling of structured data in space, time, and hierarchies derived thereof. The generative character of the presented models enables a direct synthesis of plausible hypotheses.
Extensive experiments validate the developed models in two application scenarios which are of particular interest in computer vision: human bodies and natural scenes. In the practical sections of this work we discuss both areas from different angles and show applications of our models to human pose, motion, and segmentation as well as object categorization and localization. Here, we benefit from the availability of modern datasets of unprecedented size and diversity. Comparisons to traditional approaches and state-of-the-art research on the basis of well-established evaluation criteria allows the objective assessment of our contributions.

We investigate the microswimming behaviour of robotic sperms in viscous fluids. These robotic sperms are fabricated from polystyrene dissolved in dimethyl formamide and iron-oxide nanoparticles. This composition allows the nanoparticles to be concentrated within the bead of the robotic sperm and provide magnetic dipole, whereas the flexibility of the ultra-thin tail enables flagellated locomotion using magnetic fields in millitesla range. We show that these robotic sperms have similar morphology and swimming behaviour to those of sperm cells. Moreover, we show experimentally that our robotic sperms swim controllably at an average speed of approximately one body length per second (around 125 μm s−1), and they are relatively faster than the microswimmers that depend on planar wave propulsion in low-Reynolds number fluids.

Nanoplasmonic systems are valued for their strong optical response and their small size. Most plasmonic sensors and systems to date have been rigid and passive. However, rendering these structures dynamic opens new possibilities for applications. Here we demonstrate that dynamic plasmonic nanoparticles can be used as mechanical sensors to selectively probe the rheological properties of a fluid in situ at the nanoscale and in microscopic volumes. We fabricate chiral magneto-plasmonic nanocolloids that can be actuated by an external magnetic field, which in turn allows for the direct and fast modulation of their distinct optical response. The method is robust and allows nanorheological measurements with a mechanical sensitivity of similar to 0.1 cP, even in strongly absorbing fluids with an optical density of up to OD similar to 3 (similar to 0.1% light transmittance) and in the presence of scatterers (e.g., 50% v/v red blood cells).

Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body “scanning” practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

Existing remotely actuated magnetic microrobots exhibit a maximum of only five-degree-of-freedom (DOF) actuation, as creation of a driving torque about the microrobot magnetization axis is not achievable. This lack of full orientation control limits the effectiveness of existing microrobots for precision tasks of object manipulation and orientation for advanced medical, biological and micromanufacturing applications. This paper presents a magnetic actuation method that allows remotely powered microrobots to achieve full six-DOF actuation by considering the case of a non-uniform magnetization profile within the microrobot body. This non-uniform magnetization allows for additional rigid-body torques to be induced from magnetic forces via a moment arm. A general analytical model presents the working principle for continuous and discrete magnetization profiles, which is applied to permanent or non-permanent (soft) magnet bodies. Several discrete-magnetization designs are also presented which possess reduced coupling between magnetic forces and induced rigid-body torques. Design guidelines are introduced which can be followed to ensure that a magnetic microrobot design is capable of six-DOF actuation. A simple permanent-magnet prototype is fabricated and used to quantitatively demonstrate the accuracy of the analytical model in a constrained-DOF environment and qualitatively for free motion in a viscous liquid three-dimensional environment. Results show that desired forces and torques can be created with high precision and limited parasitic actuation, allowing for full six-DOF actuation using limited feedback control

International Journal of Computer Vision (IJCV), 118(2):172-193, June 2016 (article)

Abstract

Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.

Gallium exhibits highly reversible and switchable adhesion when it undergoes a solid–liquid phase transition. The robustness of gallium is notable as it exhibits strong performance on a wide range of smooth and rough surfaces, under both dry and wet conditions. Gallium may therefore find numerous applications in transfer printing, robotics, electronic packaging, and biomedicine.

A surface patterning technique and a specific and strong biotin–streptavidin bonding of bacteria on patterned surfaces are proposed to fabricate Janus particles that are propelled by the attached bacteria. Bacteria-driven Janus microswimmers with diameters larger than 3 μm show enhanced mean propulsion speed. Such microswimmers could be used for future applications such as targeted drug delivery and environmental remediation.

Proceedings of the National Academy of Sciences, 113(41):E6007–E6015, National Acad Sciences, May 2016 (article)

Abstract

Shape-programmable matter is a class of active materials whose geometry can be controlled to potentially achieve mechanical functionalities beyond those of traditional machines. Among these materials, magnetically actuated matter is particularly promising for achieving complex time-varying shapes at small scale (overall dimensions smaller than 1 cm). However, previous work can only program these materials for limited applications, as they rely solely on human intuition to approximate the required magnetization profile and actuating magnetic fields for their materials. Here, we propose a universal programming methodology that can automatically generate the required magnetization profile and actuating fields for soft matter to achieve new time-varying shapes. The universality of the proposed method can therefore inspire a vast number of miniature soft devices that are critical in robotics, smart engineering surfaces and materials, and biomedical devices. Our proposed method includes theoretical formulations, computational strategies, and fabrication procedures for programming magnetic soft matter. The presented theory and computational method are universal for programming 2D or 3D time-varying shapes, whereas the fabrication technique is generic only for creating planar beams. Based on the proposed programming method, we created a jellyfish-like robot, a spermatozoid-like undulating swimmer, and an artificial cilium that could mimic the complex beating patterns of its biological counterpart.

An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action).
A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g. educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system.

Since the 1950s, robotics research has sought to build a general-purpose agent capable of autonomous, open-ended interaction with realistic, unconstrained environments. Cognition is perceived to be at the core of this process, yet understanding has been challenged because cognition is referred to differently within and across research areas, and is not clearly defined. The classic robotics approach is decomposition into functional modules which perform planning, reasoning, and problem-solving or provide input to these mechanisms. Although advancements have been made and numerous success stories reported in specific niches, this systems-engineering approach has not succeeded in building such a cognitive agent.
The emergence of an action-oriented paradigm offers a new approach: action and perception are no longer separable into functional modules but must be considered in a complete loop. This chapter reviews work on different mechanisms for action- perception learning and discusses the role of embodiment in the design of the underlying representations and learning. It discusses the evaluation of agents and suggests the development of a new embodied Turing Test. Appropriate scenarios need to be devised in addition to current competitions, so that abilities can be tested over long time periods.

Most soft robotic systems are currently dependent on bulky compressors or pumps. A soft actuation method is presented combining hyperelastic membranes and dielectric elastomer actuators to switch between stable deformations of sealed chambers. This method is capable of large repeatable deformations, and has a number of stable states proportional to the number of actuatable membranes in the chamber.

In this study, in a bio-hybrid microswimmer system driven by multiple Serratia marcescens bacteria, we quantify the chemotactic drift of a large number of microswimmers towards L-serine and elucidate the associated collective chemotaxis behavior by statistical analysis of over a thousand swimming trajectories of the microswimmers. The results show that the microswimmers have a strong heading preference for moving up the L-serine gradient, while their speed does not change considerably when moving up and down the gradient; therefore, the heading bias constitutes the major factor that produces the chemotactic drift. The heading direction of a microswimmer is found to be significantly more persistent when it moves up the L-serine gradient than when it travels down the gradient; this effect causes the apparent heading preference of the microswimmers and is the crucial reason that enables the seemingly cooperative chemotaxis of multiple bacteria on a microswimmer. In addition, we find that their chemotactic drift velocity increases superquadratically with their mean swimming speed, suggesting that chemotaxis of bio-hybrid microsystems can be enhanced by designing and building faster microswimmers. Such bio-hybrid microswimmers with chemotactic steering capability may find future applications in targeted drug delivery, bioengineering, and lab-on-a-chip devices.

Miniature untethered medical robots have been receiving growing attention due to technological advances in microactuation, microsensors, and microfabrication and have significant potential to reduce the invasiveness and improve the accessibility of medical devices into unprecedented small spaces inside the human body. In this review, we discuss therapeutic and diagnostic applications of untethered medical microrobots. Wirelessly controlled milli/microrobots with integrated sensors are revolutionizing micromanipulation based medical interventions and are enabling doctors to perform minimally invasive procedures not possible before. 3D fabrication technologies enabling milli/microrobot fabrication at the single cell scale are empowering high-resolution visual imaging and in vivo manipulation capabilities. Swallowable millirobots and injectabale ocular microrobots allow the gastric ulcer imaging, and performance of vitreoretinal microsurgery at previously inaccessible ocular sites. Many invasive excision and incision based diagnostic biopsy, prostrate, and nephrolgical procedures can be performed minimally or almost noninvasively due to recent advancements in microrobotic technology. Advances in biohybrid microrobot systems are pushing microrobotic systems even smaller, using biological cells as on-board microactuators and microsensors using the chemical energy. Such microrobotic systems could be used for local targeted delivery of imaging contrast agents, drugs, genes, and mRNA, minimally invasive surgery, and cell micromanipulation in the near future.

Biosensors based on the localized surface plasmon resonance (LSPR) of individual metallic nanoparticles promise to deliver modular, low-cost sensing with high-detection thresholds. However, they continue to suffer from relatively low sensitivity and figures of merit (FOMs). Herein we introduce the idea of sensitivity enhancement of LSPR sensors through engineering of the material dispersion function. Employing dispersion and shape engineering of chiral nanoparticles leads to remarkable refractive index sensitivities (1,091 nmRIU(-1) at lambda = 921 nm) and FOMs (>2,800 RIU-1). A key feature is that the polarization-dependent extinction of the nanoparticles is now characterized by rich spectral features, including bipolar peaks and nulls, suitable for tracking refractive index changes. This sensing modality offers strong optical contrast even in the presence of highly absorbing media, an important consideration for use in complex biological media with limited transmission. The technique is sensitive to surface-specific binding events which we demonstrate through biotin-avidin surface coupling.

There is an increasing demand for flexible, skin-attachable, and wearable strain sensors due to their various potential applications. However, achieving strain sensors with both high sensitivity and high stretchability is still a grand challenge. Here, we propose highly sensitive and stretchable strain sensors based on the reversible microcrack formation in composite thin films. Controllable parallel microcracks are generated in graphite thin films coated on elastomer films. Sensors made of graphite thin films with short microcracks possess high gauge factors (maximum value of 522.6) and stretchability (ε ≥ 50%), whereas sensors with long microcracks show ultrahigh sensitivity (maximum value of 11 344) with limited stretchability (ε ≤ 50%). We demonstrate the high performance strain sensing of our sensors in both small and large strain sensing applications such as human physiological activity recognition, human body large motion capturing, vibration detection, pressure sensing, and soft robotics.

There is a growing demand for flexible and soft electronic devices. In particular, stretchable, skin-mountable, and wearable strain sensors are needed for several potential applications including personalized health-monitoring, human motion detection, human-machine interfaces, soft robotics, and so forth. This Feature Article presents recent advancements in the development of flexible and stretchable strain sensors. The article shows that highly stretchable strain sensors are successfully being developed by new mechanisms such as disconnection between overlapped nanomaterials, crack propagation in thin films, and tunneling effect, different from traditional strain sensing mechanisms. Strain sensing performances of recently reported strain sensors are comprehensively studied and discussed, showing that appropriate choice of composite structures as well as suitable interaction between functional nanomaterials and polymers are essential for the high performance strain sensing. Next, simulation results of piezoresistivity of stretchable strain sensors by computational models are reported. Finally, potential applications of flexible strain sensors are described. This survey reveals that flexible, skin-mountable, and wearable strain sensors have potential in diverse applications while several grand challenges have to be still overcome.

In this paper, we present a methodology for the size optimization of an external magnetic system made of arc-shaped permanent magnets (ASMs). This magnetic system is able to remotely actuate a drug-release module embedded in a prototype of a capsule robot. The optimization of the magnetic system is carried out by using an accurate analytical model that is valid for any arbitrary dimensions of the ASMs. By using this analytical model, we perform parametric studies and conduct a statistical analysis [analysis of variance (ANOVA)] to investigate efficient ways to distribute the volume of the ASMs so that the dimensions and volume of the magnetic system are minimized while optimal flux densities and magnetic torques are obtained to actuate the drug delivery system (DDS). The ANOVA results, at 5% significance level, indicate that changes in the angular width followed by changes in the length of the ASMs have the highest impact on the magnetic linkage. Furthermore, our experimental results, which are in agreement with the analytical results, show that the size optimization of the magnetic system is effective for the actuation of the DDS in capsule robots.

We show that DNA-based self-assembly can serve as a general and flexible tool to construct artificial flagella of several micrometers in length and only tens of nanometers in diameter. By attaching the DNA flagella to biocompatible magnetic microparticles, we provide a proof of concept demonstration of hybrid structures that, when rotated in an external magnetic field, propel by means of a flagellar bundle, similar to self-propelling peritrichous bacteria. Our theoretical analysis predicts that flagellar bundles that possess a length-dependent bending stiffness should exhibit a superior swimming speed compared to swimmers with a single appendage. The DNA self-assembly method permits the realization of these improved flagellar bundles in good agreement with our quantitative model. DNA flagella with well-controlled shape could fundamentally increase the functionality of fully biocompatible nanorobots and extend the scope and complexity of active materials.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems